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This paper overviews existing digital communication buses which are commonly used in sensor networks, discusses sensor network architectures, and introduces a new sensor bus for low power microsystem applications. The new intra-module multi-element microsystem (IM2) bus is nine-line interface with 8b

This paper overviews existing digital communication buses which are commonly used in sensor networks, discusses sensor network architectures, and introduces a new sensor bus for low power microsystem applications. The new intra-module multi-element microsystem (IM2) bus is nine-line interface with 8b serial data which implements several advanced features such as power management and plug-n-play while maintaining minimum hardware overhead at the sensor node. Finally, some issues in wireless sensor networking are discussed. The coverage of these issues provides a guideline for choosing the appropriate bus for different sensor network applications.
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Sensor networks is among the fastest growing technologies that have the potential of changing our lives drastically. These collaborative, dynamic and distributed computing and communicating systems will be self organizing. They will have capabilities of distributing a task among themselves for efficient computation.

Sensor networks is among the fastest growing technologies that have the potential of changing our lives drastically. These collaborative, dynamic and distributed computing and communicating systems will be self organizing. They will have capabilities of distributing a task among themselves for efficient computation. There are many challenges in implementation of such systems: energy dissipation and clustering being one of them. In order to maintain a certain degree of service quality and a reasonable system lifetime, energy needs to be optimized at every stage of system operation. Sensor node clustering is another very important optimization problem. Nodes that are clustered together will easily be able to communicate with each other. Considering energy as an optimization parameter while clustering is imperative. In this paper we study the theoretical aspects of the clustering problem in sensor networks with application to energy optimization. We illustrate an optimal algorithm for clustering the sensor nodes such that each cluster (which has a master) is balanced and the total distance between sensor nodes and master nodes is minimized. Balancing the clusters is needed for evenly distributing the load on all master nodes. Minimizing the total distance helps in reducing the communication overhead and hence the energy dissipation. This problem (which we call balanced k-clustering) is modeled as a mincost flow problem which can be solved optimally using existing techniques.
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The Sensor Web is a macro-instrument concept that allows for the spatiotemporal understanding of an environment through coordinated efforts between multiple numbers and types of sensing platforms, including both orbital and terrestrial and both fixed and mobile. Each of these platforms, or pods,

The Sensor Web is a macro-instrument concept that allows for the spatiotemporal understanding of an environment through coordinated efforts between multiple numbers and types of sensing platforms, including both orbital and terrestrial and both fixed and mobile. Each of these platforms, or pods, communicates within their local neighborhood and thus distributes information to the instrument as a whole. Much as intelligence in the brain is a result of the myriad of connections between dendrites, it is anticipated that the Sensor Web will develop a macro-intelligence as a result of its distributed information with the pods reacting and adapting to their environment in a way that is much more than their individual sum. The sharing of data among individual pods will allow for a global perception and purpose of the instrument as a whole. The Sensor Web is to sensors what the Internet is to computers, with different platforms and operating systems communicating via a set of shared, robust protocols. This paper will outline the potential of the Sensor Web concept and describe the Jet Propulsion Laboratory (JPL) Sensor Webs Project (http://sensorwebs.jpl.nasa.gov/). In particular, various fielded Sensor Webs will be discussed.
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Advances in sensor technology and wireless communications have made networked microsensors possible, where each sensor individually senses the environment but collaboratively achieves complex information gathering and dissemination tasks. These networked sensors, however, possess several characteristics that have challenged many aspects of traditional computer

Advances in sensor technology and wireless communications have made networked microsensors possible, where each sensor individually senses the environment but collaboratively achieves complex information gathering and dissemination tasks. These networked sensors, however, possess several characteristics that have challenged many aspects of traditional computer network design, such as the scalability issue caused by the sheer amount of sensor nodes, the infrastructureless network, and the stringent resource onboard the sensors. These new features call for a re-design of overall structure of applications and services. It has been widely accepted that practical localized algorithms is probably the best solution to wireless sensor networks. In this article, we discuss recent research results on localized algorithms design in supporting services and applications in sensor networks.
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This paper presents a comprehensive review of magnetoelastic environmental sensor technology; topics include operating physics, sensor design, and illustrative applications. Magnetoelastic sensors are made of amorphous metallic glass ribbons or wires, with a characteristic resonant frequency inversely proportional to length. The remotely detected resonant frequency of a magnetoelastic sensor shifts in response to different physical parameters including stress, pressure, temperature, flow velocity, liquid viscosity, magnetic field, and mass loading. Coating the magnetoelastic sensor with a mass changing, chemically responsive layer enables realization of chemical sensors. Magnetoelastic sensors can be remotely interrogated by magnetic, acoustic, or optical means. The sensors can be characterized in the time domain, where the resonant frequency is determined through analysis of the sensor transient response, or in the frequency domain where the resonant frequency is determined from the frequency-amplitude spectrum of the sensor.
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